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bayess version 1.6

A R library associated to the book

Bayesian Essentials with R
Jean-Michel Marin and Christian P. Robert
Springer-Verlag, New York, 2014

The package bayess contains a collection of functions that allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations.

To install the bayess package

you can use the function install_github from the package remotes:

install_github("jmm34/bayess")

you can download the file bayess_1.6.tar.gz and use the command:

install.packages("bayess_1.6.tar.gz", repos = NULL, type = "source")

the package is also available on the CRAN

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Install

install.packages('bayess')

Monthly Downloads

147

Version

1.7

License

GPL-2

Issues

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Repository

https://github.com/jmm34/bayess

Homepage

https://www.r-project.org

Maintainer

JeanMichel Marin

Last Published

August 28th, 2026

Functions in bayess (1.7)

Metropolis--Hastings evaluation of the posterior associated with an AR(p) model

Accept-reject algorithm for the open population capture-recapture model

bank dataset (Chapter 4)

Metropolis-Hastings with tempering steps for the mean mixture posterior model

Metropolis-Hastings for the logit model under a noninformative prior

Gibbs sampler for a generic mixture posterior distribution

Gibbs sampler on a mixture posterior distribution with unknown means

probitnoinflpost

Log of the posterior density for the probit model under a non-informative model

Posterior probabilities for the multiple stage capture-recapture model

Metropolis-Hastings for the probit model under a flat prior

Gibbs sampler and Chib's evidence approximation for a generic univariate mixture of normal distributions

Log-likelihood of the probit model

Estimation of a hidden Markov model with 2 hidden and 4 observed states

European Dipper dataset

Log-likelihood of the logit model

logitnoinflpost

Log of the posterior distribution for the probit model under a noninformative prior

Posterior probabilities for the Darroch model

Random simulator for the truncated normal distribution

Pine processionary caterpillar dataset

Image reconstruction for the Potts model with six classes

Bound for the accept-reject algorithm in Chapter 5

Random generator for the Dirichlet distribution

Gibbs sampling for the Arnason-Schwarz capture-recapture model

Metropolis-Hastings for the Ising model

Metropolis-Hastings for the log-linear model under a noninformative prior

Metropolis-Hastings for the probit model under a noninformative prior

Metropolis-Hastings for the log-linear model under a flat prior

loglinnoinflpost

Log of the posterior density for the log-linear model under a noninformative prior

Non-standardised Licence dataset

Log of the likelihood of the log-linear model

Gibbs sampler for the Ising model

Number of neighbours with the same colour

Gibbs sampler for the Potts model

Graphical representation of a normal mixture log-likelihood

Metropolis-Hastings sampler for a Potts model with ncol classes

Approximation by path sampling of the normalising constant for the Ising model

Posterior expectation for the binomial capture-recapture model

Coverage of the interval \((a,b)\) by the Beta cdf

Recursive resolution of beta prior calibration

Metropolis-Hastings for the logit model under a flat prior

log-likelihood associated with an AR(p) model defined either through its natural coefficients or through the roots of the associated lag-polynomial

Bayesian linear regression output

Grey-level image of the Lake of Menteith

log-likelihood associated with an MA(p) model

DNA sequence of an HIV genome

Bayesian model choice procedure for the linear model

Metropolis--Hastings evaluation of the posterior associated with an MA(p) model

Gibbs sampler for the two-stage open population capture-recapture model

Eurostoxx50 exerpt dataset